Articles | Volume 15, issue 8
Data description paper
10 Aug 2023
Data description paper |  | 10 Aug 2023

Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method

Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-28', Anonymous Referee #1, 03 Apr 2023
    • AC1: 'Reply on RC1', Qianqqiang Yuan, 23 Jun 2023
  • RC2: 'Comment on essd-2023-28', Anonymous Referee #2, 30 May 2023
    • AC2: 'Reply on RC2', Qianqqiang Yuan, 23 Jun 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Qianqqiang Yuan on behalf of the Authors (23 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (27 Jun 2023) by Yuqiang Zhang
AR by Qianqqiang Yuan on behalf of the Authors (04 Jul 2023)  Manuscript 
Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Final-revised paper